ATD: Anomaly detection and functional data analysis with applications to threat detection for multimodal satellite data

ATD：异常检测和功能数据分析以及多模式卫星数据威胁检测的应用

• 批准号: 2319011
• 负责人: Julio Castrillon
• 金额: $ 25万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319011&HistoricalAwards=false
• 关键词: ATD; Anomaly; detection; functional; data

The combination of massive data analysis and advancements in Artificial Intelligence (AI) is causing major changes in society. One area where this is crucial is the integration of remote sensing and Geographic Information System (GIS), like satellite data, which is important for understanding the impact of human activities on the environment and climate. Deforestation is a key factor in climate change, causing negative effects on ecosystems, biodiversity, and human populations. Fortunately, there are now extensive satellite datasets that can help detect deforestation, especially in critical forests like the Amazon. However, tracking deforestation, degradation, and forest regrowth is challenging due to factors like clouds and shadows. To address these challenges, the investigators will develop an innovative approach based on a new mathematical framework. This research is relevant to various fields such as human migration, climate change, transportation logistics, and epidemic disease diffusion. The findings will be valuable for intelligence gathering and will contribute to understanding security conditions, informing assessments and decisions, including those with military and political implications. Moreover, the investigators are committed to training and nurturing students' expertise in these areas, providing them with valuable learning opportunities.The investigators will develop an innovative and distinct approach to change-point and anomaly detection within the framework of mathematical functional analysis, utilizing representations like the Karhunen-Loeve (KL) expansion. This approach deviates from previous methods in several significant ways. KL expansions are ideal for representing random processes, providing optimal representations. They exhibit a remarkable level of generality, enabling accurate representation of various processes and fields over complex geometrical domains. Detection is achieved by constructing and matching nested eigenspaces tailored to truncated KL expansions. Unlike current statistical approaches, the proposed approach is rooted in functional analysis and offers several advantages for detecting hidden phenomena in complex domains: 1) Principled detection of anomalous global and local signals. 2) Development of reliable hypothesis tests using robust concentration inequalities without making assumptions about data distributions (essential) e.g. robust statistical significance for detection. 3) Precise anomaly quantification. 4) Applicability to diverse geometries, including geo-spatial, spatio-temporal, surfaces, networks, etc. The construction of nested subspaces involves efficient algorithms derived from computational applied mathematics and high-performance computing. 5) Integration of multilevel filters capable of processing massive volumes of data with near-optimal performance. Overall, this approach, rooted in functional analysis, presents a new perspective on change and anomaly detection.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

海量数据分析和人工智能(AI)的进步相结合，正在给社会带来重大变化。其中一个至关重要的领域是遥感和地理信息系统(地理信息系统)的结合，如卫星数据，这对于了解人类活动对环境和气候的影响很重要。森林砍伐是气候变化的一个关键因素，对生态系统、生物多样性和人类人口造成负面影响。幸运的是，现在有大量的卫星数据集可以帮助检测森林砍伐，特别是像亚马逊这样的关键森林。然而，由于云层和阴影等因素，追踪森林砍伐、退化和森林再生是具有挑战性的。为了应对这些挑战，调查人员将开发一种基于新的数学框架的创新方法。这项研究涉及到人口迁移、气候变化、交通物流和疫病传播等多个领域。这些发现将对情报收集很有价值，并将有助于了解安全状况，为评估和决定提供信息，包括那些具有军事和政治影响的评估和决定。此外，调查人员致力于培训和培养学生在这些领域的专业知识，为他们提供宝贵的学习机会。调查人员将在数学泛函分析的框架内开发一种创新和独特的方法来检测变点和异常，利用卡尔胡宁-洛夫(KL)展开等表示法。这种方法在几个重要方面与以前的方法不同。KL展开非常适合于表示随机过程，从而提供最佳表示。它们表现出非凡的通用性，使得能够在复杂的几何域上准确地表示各种过程和场。通过构造和匹配针对截断KL展开量身定做的嵌套特征空间来实现检测。与目前的统计方法不同，该方法植根于泛函分析，在检测复杂领域中的隐藏现象方面具有几个优点：1)原则性地检测全局和局部异常信号。2)使用稳健的集中不等式开发可靠的假设检验，而不对数据分布做出假设(基本)，例如对于检测的稳健的统计意义。3)精确的异常量化。4)适用于不同的几何，包括地理空间、时空、表面、网络等。嵌套子空间的构造涉及计算应用数学和高性能计算的高效算法。5)集成了能够以近乎最佳的性能处理海量数据的多级过滤器。总体而言，这种植根于功能分析的方法为变化和异常检测提供了一个新的视角。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。